S3 Analysis of Longitudinal Study of Australian Children

Introduction

Growing up Australia : Longitudinal Study of Australian Children (LSAC) aims to examine the impact of the Australian social, economic, and cultural environment over the life course to identify the opportunities for early intervention. LSAC is a collaboration between the Australian Institute of Family Studies and the Australian Bureau of Statistics with advice from leading researchers in the form of a Consortium Advisory Group.

Recruitment for LSAC was undertaken between March and November 2004, with over 10,000 families agreeing to participate. Since 2004, data has been collected every two years, with mail-out and online questionnaires set out between each main collection. The study recruited from two cohorts, the birth cohort (B) where children were born between March 2003 and February 2004, and the kindergarten cohort (K) where children were born between March 1999 and February 2000, i.e. in kindergarten at the time of recruitment.

Table 1 shows the sample size over collection waves and years. There was a reduced set of data collected in wave 9.1 due to COVID-19 restrictions. This data did not have measured heights and weights at is removed from the analysis for DiSCAO.

cohort Wave 1 2004 Wave 2 2006 Wave 3 2008 Wave 4 2010 Wave 5 2012 Wave 6 2014 Wave 7 2016 Wave 8 2018 Wave 9.1 2020
B 5107 4603 4386 4231 4085 3758 3375 3117 2017
K 4980 4459 4331 4161 3956 3529 3076 3023 1789

Figure 1, shows how the LSAC sample was collected using the dual cohort design, this allows multiple measurements of age groups separated by 4 years or 2 collection waves.

Figure 1: dual cohort cross sectional design of LSAC, Australian Institute of Family Studies (2018)

Figure 1: dual cohort cross sectional design of LSAC, Australian Institute of Family Studies (2018)

Australian Institute of Family Studies. (2018). Longitudinal Study of Australian Children Data User Guide – December 2018. Melbourne: Australian Institute of Family Studies.

The health check “CheckPoint” data collection was a one-off physical health and biomarker module added for the birth cohort (B) between the LSAC waves 6 and 7, collected between February 2015 and March 2016. The B cohort study child and one of their parents were invited to participate in a clinical appointment or short home visit. The B cohort study children were between the ages of 11-12 at the time of measurement.

Body composition data was collected in waves 4 - 8, however, due to larger variances in fat mass and a large number of responses being 100% or 0% fat mass, this data was used for validation of analysis using the CheckPoint data.

Methods

Descriptive summaries

Data was visually summarised with plots by age-gender-BMI groups. Medians and confidence, weighted to adjust for sampling strategies, summarised are used as inputs for the DiSCAO and presented in the Appendix.

Estimating % Fat mass

Body composition is a required input into the energy regulation equations presented by Hall et al 2013 and used in Chapter 4 Section 4.2.2.3.

\[ \Delta FFM_{i,j,k}=\frac{pEnS_{i.j.k}+G_{j,k}}{\hat{\rho_{FFM, i,j,k}}}\] and

\[ \Delta FM_{i,j,k}=\frac{(1-p_{i,j,k})EnS_{i.j.k}-G_{j,k}}{\rho_{FM}}\] Such that \(\hat{\rho_{FFM, i,j,k}}\) and \(p_{i,j,k}\) rely on estimates of body composition.

\[ \hat{\rho_{FFM, i,j,k}} = (837+4.3 \times FFM_{i,j,k}) \times 4.18\]

With the p-ratio defined by Forbes 1987. \[ p_{i,j,k} = \frac{C_{i,j,k}}{C_{i,j,k}+FM_{i.j.k}}\]

and where,

\[C_{i,j,k}=10.4 \times\frac{\hat{\rho_{FFM, i,j,k}}}{\rho_{FM}}\]

\(i=\{ \text{BMI categoried} \}\)

\(j=\{ \text{Age groups} \}\)

\(k=\{ \text{Gender} \}\)

these equations were constructed using linear regression assuming normal distribution errors on a relatively small sample (n=416). These equations will also need to be applied to the main LSAC waves to give FFM% estimates for the required subgroups Linear regression was not appropriate for extrapolation.

Beta regression was used to develop equations to predict the %FFM for the “Checkpoint” data collection. These equations are then applied to the general LSAC data to estimate the FFM% for all sub-groups needed for the model.

A step-wise algorithm was used to sequentially test model terms ranging including combinations of gender, height, weight, and 2-way and 3-way interactions. Additionally, polynomial terms for height and weight were considered up to the 3rd order (height^3), this gives the model freedom to choose non-linear relationships.

The predicted %FFM was estimated using the Coretes-Castell equations and the beta regression to assess the appropriateness of each method.

Results

Descriptive summaries

Individual level BMI

These histograms of BMI over time (Figure 2) and gender-age-groups show the age sampling of the dual cohort of LSAC. The shift in shape is a key underlying dynamics that reflects changes in the prevalence of obesity in Australian children.

Figure 2: Comparison of BMI distribution over time and by gender and age groups

Figure 2: Comparison of BMI distribution over time and by gender and age groups

Body weight

Figure 3 shows the reported body weight over age, by gender and BMI categories. In general body weight increases with age, overweight and obese body weights increase at a faster rate and males have a higher body weight than females. It should be noted that the data is presented as cross-sectional when there are repeat measures.

Figure 3: Combined wave, Body weight by age, gender and BMI category

Figure 3: Combined wave, Body weight by age, gender and BMI category

Figure 4 plots reported body weight from the national health survey (NHS) for all ages.

Figure 4: 2007 National Health Survey: Body weight by age, gender and BMI category

Figure 4: 2007 National Health Survey: Body weight by age, gender and BMI category

Height

Figure 5 plots the age-gender-BMI hieghts in the LSAC data.
Figure 5: Combined wave, Height by age, gender and BMI category

Figure 5: Combined wave, Height by age, gender and BMI category

Figure 6: 2007 National Health Survey: Height by age, gender and BMI category

Figure 6: 2007 National Health Survey: Height by age, gender and BMI category

Body composition

LSAC CheckPoint data

Figures 7 and 8 are 3D plots of the LSAC CheckPoint data for males and females. These plots can be examined by spinning the plot areas to look at the relationships between body weight, height and %fat-mass.

Figure 7: 3D plot of Males Height, weight and % fat-mass for LSAC CheckPoint data

Figure 8: 3D plot of Females Height, weight and % fat-mass for LSAC CheckPoint data

Beta-Regression model

The beta-regression coefficients were fitted for the health CheckPoint data using weight and height. The resulting equations are used to predict the % fat mass for each gender and are applied to the whole LSAC data. The following sections examine model performance.

Males

\[ FM_{Males} = \text{Exp}(-5.236806 - \text{weight}^{2}\times 1.057483e^{-3} - \text{height}^2 \times 2.921958e^{-4} \\ + \text{weight} \times 1.319295e^{-1} + \text{height} \times 4.529665e^{-2} + \text{weight}^2 \times \text{height}^2 \times 1.690558e^{-8} - \text{weight} \times \text{height} \times 1.492729e^{-4})\]

Females

\[ FM_{females} = \text{Exp}(-5.236806 - \text{weight}^{2}\times 1.057483e^{-3} - \text{height}^2 \times 2.921958e^{-4} + \text{weight} \times 1.319295e^{-1} \\ + \text{height} \times 4.529665e^{-2} + \text{weight}^2 \times \text{height}^2 \times 1.690558e^{-8} - \text{weight} \times \text{height} \times 1.492729e^{-4} \\ + 7.542881e^{-2} - \text{weight}^{2}\times 2.553074e^{-4} + \text{height}^2 \times 7.785382e^{-6} + \text{weight}^2 \times \text{height}^2 \times 7.933846e^{-9} )\]

Residuals

Beta regression The fitted vs residual plot (Figure 8) shows a generally good fit with a slight under estimate in higher %fat percentage and overestimates in low %fat percentage. A good fit would be seen when the red loess line follows the dashed linear (x=y) line.

Figure 8: Beta regression residuals against fitted obseravtions

The distribution of the beta regression residuals are shown in Figure 9.

Figure 9: Distribution beta regression residuals

Bland-Altman Plot

The Bland-Altman plot, shows the difference between observed and predicted against observed fat percentage in the CheckPoint data.

Figure 10: Bland-Altman plots residuls vs observed

Figure 10: Bland-Altman plots residuls vs observed

Extrapolation

The beta regression model was used to predict the remaining LSAC data. Figure 11 shows the age-gender-BMI curves for predicted % fat mass.

Figure 11: Extrapolation of Beta regression model onto LSAC data

Figure 11: Extrapolation of Beta regression model onto LSAC data

Beta Predictions againts observed LSAC wave 4-8 data

As mentioned in the methods, the main wave of the LSAC data has %fat mass collected between waves 4-8. Figure 12 shows the fit of the Beta regression predictions against the wave 4-8 data collected. The plot has coloured points by “Data quality”, observed fat-mass from waves 4-8 with fat-mass = 0 or greater than 80% was considered to be possibly an error.

The plot shows that the predicted estimates follow the linear line with deviations in the upper and lower ends.

Figure 12: Comparison of beta regression predictions against observed LSAC data

Figure 12: Comparison of beta regression predictions against observed LSAC data

Comparison to published % Fat mass data

Below are estimated % fat mass curves using the National Health and Nutrition Examination Survey (NHANES) IV (Laurson, Eisenmann and Welk, 2011,https://www.cooperinstitute.org/vault/2440/web/files/787.pdf ). we can see that the plots have a close resemblance to the predicted beta regression estimates.

The predictions generally follow the shape of NHANES data (Figure 13 vs Figure 14), over similar age ranges.

Figure 13: % fat mass curves using National Health and Nutrition Examination Survey (NHANES)

Figure 13: % fat mass curves using National Health and Nutrition Examination Survey (NHANES)

Figure 14: Quantile estimates of beta regression predictions

Figure 14: Quantile estimates of beta regression predictions

Quantile output tables

Laurson, Eisenmann and Welk: NHANES LMS Estimates

Similarly, NHANES LMS Estimates percentiles shows some agreement between published and predicted % fat mass.

Figure 15: Quantile estimates of % fat mass curves using National Health and Nutrition Examination Survey (NHANES)

Figure 15: Quantile estimates of % fat mass curves using National Health and Nutrition Examination Survey (NHANES)

From Beta Regression
gender age p_2 p_5 p_10 p_25 p_50 p_75 p_85 p_90 p_95 p_98
Male 2 10.38 10.9 11.47 12.34 13.51 14.79 15.56 16.14 17.1 18.53
Male 3 10.68 11.4 11.91 12.75 13.92 15.43 16.28 17.09 18.37 19.93
Male 4 11.71 12.39 13.03 14.07 15.42 16.98 18.06 19 20.9 24.45
Male 5 11.56 12.38 13.11 14.35 15.72 17.48 18.81 20.05 22.74 27.19
Male 6 12.18 12.95 13.66 14.88 16.46 18.73 20.55 22.28 25.81 31.17
Male 7 11.91 12.84 13.49 14.83 16.53 18.88 20.75 23.53 28.45 35.85
Male 8 12.25 13.15 13.89 15.53 17.72 21.32 24.97 27.66 33.73 40.69
Male 9 11.95 12.99 13.88 15.54 18.33 22.69 26.63 30.03 37.32 44.18
Male 10 12.31 13.29 14.26 16.31 19.52 25.52 29.78 33.24 39.33 46.28
Male 11 11.91 13.34 14.04 16.13 19.74 27.08 30.91 33.39 38.01 45.62
Male 12 11.14 12.37 13.46 15.74 19.16 25.4 30.4 33.69 38.67 43.75
Male 13 10.49 11.7 13.16 15.32 18.98 25.3 30.14 32.99 36.31 40.27
Male 14 10.35 11.39 12.73 14.87 18.68 24.78 29 31.08 34.58 38.16
Male 15 9.78 11.22 12.6 15.07 18.85 25.17 28.81 31.5 34.22 36.24
Male 16 10.01 11.67 12.92 15.48 19.19 25.26 28.31 30.56 32.2 35.62
Male 17 9.98 11.33 13.02 16.07 20.86 26.79 29.8 31.39 33.3 35.84
Female 2 11.24 11.8 12.33 13.2 14.31 15.67 16.49 17.23 18.31 19.86
Female 3 11.35 12.03 12.56 13.5 14.63 16.2 17.11 17.8 18.79 20.77
Female 4 12.84 13.57 14.21 15.3 16.73 18.52 19.76 20.98 23.16 26
Female 5 12.93 13.78 14.32 15.61 17.06 19.06 20.62 21.59 23.84 27.49
Female 6 13.66 14.46 15.2 16.55 18.31 20.68 22.42 24.2 27.99 32.81
Female 7 13.8 14.46 15.26 16.74 18.64 21.47 23.82 25.84 30.17 35.18
Female 8 13.83 15.03 15.88 17.7 20.18 24.24 27.21 29.71 34.42 39.25
Female 9 13.51 14.7 15.64 17.57 20.48 25.12 28.75 31.28 35.06 41.75
Female 10 13.34 14.74 15.82 18.26 21.65 27.06 31.96 34.6 38.91 42.15
Female 11 13.55 14.72 16.2 18.58 22.39 28.46 31.95 34.21 38.43 41.29
Female 12 13.22 14.9 16.46 19.44 23.74 29.43 33.26 35.87 39.26 41.03
Female 13 13.79 15.38 16.82 20.18 24.61 30.74 34.18 37.05 39.74 40.64
Female 14 13.95 16.34 18.36 21.93 26.15 31.96 35.97 38.37 39.83 41.14
Female 15 15.71 16.71 19.41 23.08 27.73 33.09 35.99 37.83 40.02 41.29
Female 16 15.5 17.51 19.77 23.87 28.73 34.75 38.19 39.29 40.58 41.36
Female 17 16.57 18.34 19.77 24.07 29.14 35.21 38.11 39.69 40.46 41.56

Conclusion

In this report we present the analysis of the Growing up Australia : Longitudinal Study of Australian Children (LSAC) data. The report summarises the body weight, height and % fat mass used as input for the DiSCAO model. There was much consideration when estimating the % fat mass. Ultimately, beta regression was used to estimate the %fat-mass using body weight and height relationships. Where main wave LSAC estimates of %fat-mass were used as validation of beta-regression predictions. These estimates were shown to follow published estimates, suggesting good internal and external validity. All numerical estimates used as inputs and uncertainty are presented in the appendix.

Appendix : Input variables for DiSCAO

Weight

Gender Age Groups BMI Estimate lcl ucl
Male Age 2 Underweight & Healthy weight 14.30 14.20 14.45
Male Age 2 Overweight 16.20 16.10 16.40
Male Age 2 With obesity 18.00 17.60 18.70
Male Age 3 5 Underweight & Healthy weight 18.10 18.00 18.20
Male Age 3 5 Overweight 20.70 20.55 20.90
Male Age 3 5 With obesity 24.50 24.20 24.90
Male Age 6 8 Underweight & Healthy weight 24.65 24.50 24.80
Male Age 6 8 Overweight 30.60 30.35 31.05
Male Age 6 8 With obesity 40.10 39.00 42.00
Male Age 9 11 Underweight & Healthy weight 33.80 33.70 34.10
Male Age 9 11 Overweight 45.30 44.90 46.00
Male Age 9 11 With obesity 60.70 59.20 62.50
Male Age 12 14 Underweight & Healthy weight 47.10 46.80 47.60
Male Age 12 14 Overweight 64.10 63.30 65.00
Male Age 12 14 With obesity 85.00 82.60 87.60
Male Age 15 17 Underweight & Healthy weight 63.90 63.30 64.40
Male Age 15 17 Overweight 82.20 80.80 83.90
Male Age 15 17 With obesity 106.50 101.70 109.80
Male Age 18 19 Underweight & Healthy weight 70.00 69.00 74.00
Male Age 18 19 Overweight 85.00 84.00 88.00
Male Age 18 19 With obesity 108.00 104.00 114.00
Male Age 20 24 Underweight & Healthy weight 70.00 69.00 74.00
Male Age 20 24 Overweight 85.00 84.00 88.00
Male Age 20 24 With obesity 108.00 104.00 114.00
Male Age 25 29 Underweight & Healthy weight 72.00 70.00 75.00
Male Age 25 29 Overweight 85.00 83.00 89.00
Male Age 25 29 With obesity 104.00 99.00 112.00
Male Age 30 34 Underweight & Healthy weight 72.00 71.00 75.00
Male Age 30 34 Overweight 86.00 84.00 88.00
Male Age 30 34 With obesity 105.00 104.00 111.00
Male Age 35 39 Underweight & Healthy weight 72.00 70.00 75.00
Male Age 35 39 Overweight 87.00 86.00 89.00
Male Age 35 39 With obesity 103.00 102.00 107.00
Male Age 40 44 Underweight & Healthy weight 72.00 70.00 75.00
Male Age 40 44 Overweight 86.00 85.00 88.00
Male Age 40 44 With obesity 103.00 100.00 107.00
Male Age 45 49 Underweight & Healthy weight 71.00 70.00 73.00
Male Age 45 49 Overweight 85.00 84.00 88.00
Male Age 45 49 With obesity 102.00 101.00 109.00
Female Age 2 Underweight & Healthy weight 13.60 13.55 13.75
Female Age 2 Overweight 15.65 15.50 15.90
Female Age 2 With obesity 17.60 17.30 18.10
Female Age 3 5 Underweight & Healthy weight 17.45 17.40 17.55
Female Age 3 5 Overweight 20.45 20.30 20.60
Female Age 3 5 With obesity 24.80 24.30 25.45
Female Age 6 8 Underweight & Healthy weight 24.20 24.10 24.40
Female Age 6 8 Overweight 31.10 30.70 31.45
Female Age 6 8 With obesity 40.20 39.30 41.60
Female Age 9 11 Underweight & Healthy weight 34.00 33.70 34.30
Female Age 9 11 Overweight 47.30 46.70 48.00
Female Age 9 11 With obesity 62.40 60.00 64.70
Female Age 12 14 Underweight & Healthy weight 48.60 48.20 49.00
Female Age 12 14 Overweight 64.20 63.60 65.00
Female Age 12 14 With obesity 86.10 83.00 87.80
Female Age 15 17 Underweight & Healthy weight 56.60 56.10 57.30
Female Age 15 17 Overweight 73.60 72.60 75.00
Female Age 15 17 With obesity 97.70 93.70 99.60
Female Age 18 19 Underweight & Healthy weight 58.00 57.00 60.00
Female Age 18 19 Overweight 72.00 70.00 74.00
Female Age 18 19 With obesity 91.00 89.00 98.00
Female Age 20 24 Underweight & Healthy weight 58.00 57.00 60.00
Female Age 20 24 Overweight 72.00 70.00 74.00
Female Age 20 24 With obesity 91.00 89.00 98.00
Female Age 25 29 Underweight & Healthy weight 59.00 58.00 62.00
Female Age 25 29 Overweight 74.00 72.00 76.00
Female Age 25 29 With obesity 91.00 89.00 97.00
Female Age 30 34 Underweight & Healthy weight 59.00 58.00 61.00
Female Age 30 34 Overweight 72.00 71.00 75.00
Female Age 30 34 With obesity 94.00 92.00 102.00
Female Age 35 39 Underweight & Healthy weight 58.00 57.00 60.00
Female Age 35 39 Overweight 71.00 70.00 73.00
Female Age 35 39 With obesity 88.00 85.00 93.00
Female Age 40 44 Underweight & Healthy weight 59.00 59.00 62.00
Female Age 40 44 Overweight 73.00 71.00 76.00
Female Age 40 44 With obesity 91.00 90.00 96.00
Female Age 45 49 Underweight & Healthy weight 59.00 58.00 61.00
Female Age 45 49 Overweight 72.00 71.00 74.00
Female Age 45 49 With obesity 88.00 86.00 91.00

Heights

Gender Age Groups BMI Estimate lcl ucl
Male Age 2 Underweight & Healthy weight 94.00 93.90 94.25
Male Age 2 Overweight 94.45 94.00 95.00
Male Age 2 With obesity 95.10 94.00 96.00
Male Age 3 5 Underweight & Healthy weight 108.00 107.90 108.30
Male Age 3 5 Overweight 108.60 108.40 109.00
Male Age 3 5 With obesity 111.45 110.50 112.00
Male Age 6 8 Underweight & Healthy weight 125.35 125.05 125.65
Male Age 6 8 Overweight 127.70 127.35 128.10
Male Age 6 8 With obesity 130.55 129.70 131.70
Male Age 9 11 Underweight & Healthy weight 141.90 141.40 142.15
Male Age 9 11 Overweight 144.75 144.30 145.30
Male Age 9 11 With obesity 147.25 145.80 148.95
Male Age 12 14 Underweight & Healthy weight 159.90 159.40 160.40
Male Age 12 14 Overweight 162.45 161.90 163.35
Male Age 12 14 With obesity 164.40 162.80 166.40
Male Age 15 17 Underweight & Healthy weight 176.00 175.50 176.60
Male Age 15 17 Overweight 176.50 175.35 177.40
Male Age 15 17 With obesity 177.30 175.35 179.60
Male Age 18 19 Underweight & Healthy weight 178.00 177.00 182.00
Male Age 18 19 Overweight 179.00 178.00 180.00
Male Age 18 19 With obesity 181.00 179.00 184.00
Male Age 20 24 Underweight & Healthy weight 178.00 177.00 182.00
Male Age 20 24 Overweight 179.00 178.00 180.00
Male Age 20 24 With obesity 181.00 179.00 184.00
Male Age 25 29 Underweight & Healthy weight 178.00 177.00 180.00
Male Age 25 29 Overweight 178.00 176.00 180.00
Male Age 25 29 With obesity 178.00 175.00 182.00
Male Age 30 34 Underweight & Healthy weight 178.00 177.00 181.00
Male Age 30 34 Overweight 179.00 178.00 181.00
Male Age 30 34 With obesity 179.00 177.00 183.00
Male Age 35 39 Underweight & Healthy weight 177.00 176.00 178.00
Male Age 35 39 Overweight 179.00 178.00 181.00
Male Age 35 39 With obesity 177.00 175.00 179.00
Male Age 40 44 Underweight & Healthy weight 176.00 175.00 178.00
Male Age 40 44 Overweight 177.00 176.00 178.00
Male Age 40 44 With obesity 177.00 175.00 179.00
Male Age 45 49 Underweight & Healthy weight 175.00 174.00 177.00
Male Age 45 49 Overweight 176.00 175.00 178.00
Male Age 45 49 With obesity 176.00 175.00 178.00
Female Age 2 Underweight & Healthy weight 92.50 92.30 92.90
Female Age 2 Overweight 93.30 93.00 93.75
Female Age 2 With obesity 93.30 92.25 94.75
Female Age 3 5 Underweight & Healthy weight 106.80 106.55 107.00
Female Age 3 5 Overweight 108.00 107.60 108.50
Female Age 3 5 With obesity 110.00 109.05 111.00
Female Age 6 8 Underweight & Healthy weight 124.25 124.00 124.60
Female Age 6 8 Overweight 127.15 126.60 127.65
Female Age 6 8 With obesity 129.80 128.90 131.00
Female Age 9 11 Underweight & Healthy weight 141.50 141.20 141.95
Female Age 9 11 Overweight 145.75 145.00 146.55
Female Age 9 11 With obesity 147.30 146.00 148.95
Female Age 12 14 Underweight & Healthy weight 159.00 158.80 159.50
Female Age 12 14 Overweight 160.50 159.95 161.10
Female Age 12 14 With obesity 161.90 160.60 163.55
Female Age 15 17 Underweight & Healthy weight 164.15 163.70 164.70
Female Age 15 17 Overweight 164.05 162.80 164.85
Female Age 15 17 With obesity 164.50 163.50 167.30
Female Age 18 19 Underweight & Healthy weight 164.00 163.00 165.00
Female Age 18 19 Overweight 164.00 163.00 167.00
Female Age 18 19 With obesity 163.00 159.00 168.00
Female Age 20 24 Underweight & Healthy weight 164.00 163.00 165.00
Female Age 20 24 Overweight 164.00 163.00 167.00
Female Age 20 24 With obesity 163.00 159.00 168.00
Female Age 25 29 Underweight & Healthy weight 164.00 164.00 166.00
Female Age 25 29 Overweight 164.00 163.00 166.00
Female Age 25 29 With obesity 165.00 165.00 169.00
Female Age 30 34 Underweight & Healthy weight 165.00 165.00 167.00
Female Age 30 34 Overweight 164.00 163.00 166.00
Female Age 30 34 With obesity 165.00 164.00 169.00
Female Age 35 39 Underweight & Healthy weight 163.00 162.00 165.00
Female Age 35 39 Overweight 163.00 162.00 165.00
Female Age 35 39 With obesity 163.00 161.00 165.00
Female Age 40 44 Underweight & Healthy weight 163.00 161.00 165.00
Female Age 40 44 Overweight 163.00 162.00 166.00
Female Age 40 44 With obesity 162.00 161.00 165.00
Female Age 45 49 Underweight & Healthy weight 164.00 164.00 165.00
Female Age 45 49 Overweight 161.00 160.00 163.00
Female Age 45 49 With obesity 162.00 160.00 164.00

% Fat-mass

Gender Age Groups BMI % FM LCL UCL
Female Age 2 Underweight & Healthy weight 13.77 13.68 13.85
Female Age 2 Overweight 16.42 16.25 16.59
Female Age 2 with Obese 19.83 19.03 20.62
Male Age 2 Underweight & Healthy weight 12.99 12.91 13.07
Male Age 2 Overweight 15.74 15.58 15.89
Male Age 2 with Obese 18.38 17.70 19.06
Female Age 3 5 Underweight & Healthy weight 15.83 15.76 15.89
Female Age 3 5 Overweight 19.76 19.64 19.88
Female Age 3 5 with Obese 26.10 25.55 26.66
Male Age 3 5 Underweight & Healthy weight 14.70 14.64 14.75
Male Age 3 5 Overweight 18.50 18.39 18.61
Male Age 3 5 with Obese 25.25 24.60 25.91
Female Age 6 8 Underweight & Healthy weight 18.16 18.08 18.25
Female Age 6 8 Overweight 25.60 25.43 25.77
Female Age 6 8 with Obese 35.47 34.80 36.14
Male Age 6 8 Underweight & Healthy weight 16.28 16.21 16.36
Male Age 6 8 Overweight 23.96 23.77 24.14
Male Age 6 8 with Obese 36.23 35.45 37.02
Female Age 9 11 Underweight & Healthy weight 19.68 19.52 19.85
Female Age 9 11 Overweight 30.79 30.50 31.08
Female Age 9 11 with Obese 40.52 39.72 41.32
Male Age 9 11 Underweight & Healthy weight 17.60 17.44 17.75
Male Age 9 11 Overweight 28.88 28.60 29.17
Male Age 9 11 with Obese 43.52 42.47 44.58
Female Age 12 14 Underweight & Healthy weight 22.25 22.06 22.45
Female Age 12 14 Overweight 34.50 34.26 34.75
Female Age 12 14 with Obese 40.12 39.64 40.61
Male Age 12 14 Underweight & Healthy weight 17.39 17.23 17.56
Male Age 12 14 Overweight 29.66 29.33 30.00
Male Age 12 14 with Obese 38.86 38.20 39.51
Female Age 15 17 Underweight & Healthy weight 25.34 25.01 25.66
Female Age 15 17 Overweight 36.85 36.59 37.12
Female Age 15 17 with Obese 39.59 38.95 40.24
Male Age 15 17 Underweight & Healthy weight 17.47 17.19 17.75
Male Age 15 17 Overweight 28.69 28.28 29.09
Male Age 15 17 with Obese 32.89 32.17 33.61
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## 
## loaded via a namespace (and not attached):
##  [1] DBI_1.1.3            gridExtra_2.3        inline_0.3.19       
##  [4] sandwich_3.0-2       rlang_1.1.1          magrittr_2.0.3      
##  [7] matrixStats_1.0.0    compiler_4.3.2       loo_2.6.0           
## [10] mgcv_1.9-0           flexmix_2.3-19       systemfonts_1.0.4   
## [13] callr_3.7.3          vctrs_0.6.3          rvest_1.0.3         
## [16] pkgconfig_2.0.3      crayon_1.5.2         fastmap_1.1.1       
## [19] ellipsis_0.3.2       backports_1.4.1      rmdformats_1.0.4    
## [22] labeling_0.4.2       utf8_1.2.3           rmarkdown_2.24      
## [25] tzdb_0.4.0           ps_1.7.5             densEstBayes_1.0-2.2
## [28] xfun_0.40            modeltools_0.2-23    cachem_1.0.8        
## [31] jsonlite_1.8.7       highr_0.10           parallel_4.3.2      
## [34] prettyunits_1.1.1    R6_2.5.1             bslib_0.5.1         
## [37] stringi_1.7.12       StanHeaders_2.26.28  car_3.1-2           
## [40] lmtest_0.9-40        jquerylib_0.1.4      Rcpp_1.0.11         
## [43] bookdown_0.35.1      rstan_2.26.23        knitr_1.44          
## [46] splines_4.3.2        nnet_7.3-19          timechange_0.2.0    
## [49] tidyselect_1.2.0     rstudioapi_0.15.0    abind_1.4-5         
## [52] yaml_2.3.7           codetools_0.2-19     processx_3.8.2      
## [55] pkgbuild_1.4.2       lattice_0.22-5       plyr_1.8.8          
## [58] withr_2.5.0          evaluate_0.21        RcppParallel_5.1.7  
## [61] zip_2.3.0            xml2_1.3.5           carData_3.0-5       
## [64] stats4_4.3.2         generics_0.1.3       hms_1.1.3           
## [67] rstantools_2.3.1.1   munsell_0.5.0        scales_1.2.1        
## [70] jmvcore_2.4.7        glue_1.6.2           lazyeval_0.2.2      
## [73] tools_4.3.2          data.table_1.14.8    SparseM_1.81        
## [76] webshot_0.5.5        ggsignif_0.6.4       mitools_2.4         
## [79] crosstalk_1.2.1      QuickJSR_1.0.6       colorspace_2.1-0    
## [82] nlme_3.1-163         Formula_1.2-5        cli_3.6.1           
## [85] fansi_1.0.4          viridisLite_0.4.2    svglite_2.1.1       
## [88] gtable_0.3.4         rstatix_0.7.2        sass_0.4.7          
## [91] digest_0.6.33        htmlwidgets_1.6.2    farver_2.1.1        
## [94] htmltools_0.5.6      lifecycle_1.0.3      httr_1.4.7